Overview

Dataset statistics

Number of variables23
Number of observations14546
Missing cells34211
Missing cells (%)10.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory192.0 B

Variable types

DateTime1
Categorical4
Numeric16
Boolean2

Alerts

MinTemp has 157 (1.1%) missing valuesMissing
Rainfall has 330 (2.3%) missing valuesMissing
Evaporation has 6183 (42.5%) missing valuesMissing
Sunshine has 6904 (47.5%) missing valuesMissing
WindGustDir has 1056 (7.3%) missing valuesMissing
WindGustSpeed has 1048 (7.2%) missing valuesMissing
WindDir9am has 1057 (7.3%) missing valuesMissing
WindDir3pm has 411 (2.8%) missing valuesMissing
WindSpeed9am has 179 (1.2%) missing valuesMissing
WindSpeed3pm has 302 (2.1%) missing valuesMissing
Humidity9am has 286 (2.0%) missing valuesMissing
Humidity3pm has 457 (3.1%) missing valuesMissing
Pressure9am has 1537 (10.6%) missing valuesMissing
Pressure3pm has 1522 (10.5%) missing valuesMissing
Cloud9am has 5558 (38.2%) missing valuesMissing
Cloud3pm has 5875 (40.4%) missing valuesMissing
Temp9am has 194 (1.3%) missing valuesMissing
Temp3pm has 361 (2.5%) missing valuesMissing
RainToday has 330 (2.3%) missing valuesMissing
RainTomorrow has 329 (2.3%) missing valuesMissing
Rainfall has 9084 (62.5%) zerosZeros
Sunshine has 244 (1.7%) zerosZeros
WindSpeed9am has 869 (6.0%) zerosZeros
Cloud9am has 852 (5.9%) zerosZeros
Cloud3pm has 498 (3.4%) zerosZeros

Reproduction

Analysis started2024-06-06 18:09:51.279555
Analysis finished2024-06-06 18:10:35.470065
Duration44.19 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Date
Date

Distinct3135
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size227.3 KiB
Minimum2007-11-27 00:00:00
Maximum2017-06-25 00:00:00
2024-06-06T20:10:35.599769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:35.837205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Location
Categorical

Distinct49
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size227.3 KiB
Sydney
 
362
Darwin
 
335
Perth
 
333
SalmonGums
 
331
SydneyAirport
 
328
Other values (44)
12857 

Length

Max length16
Median length11
Mean length8.7224667
Min length4

Characters and Unicode

Total characters126877
Distinct characters40
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMountGambier
2nd rowSydney
3rd rowMelbourne
4th rowRichmond
5th rowSydney

Common Values

ValueCountFrequency (%)
Sydney 362
 
2.5%
Darwin 335
 
2.3%
Perth 333
 
2.3%
SalmonGums 331
 
2.3%
SydneyAirport 328
 
2.3%
Canberra 327
 
2.2%
Townsville 326
 
2.2%
Bendigo 325
 
2.2%
Richmond 324
 
2.2%
Watsonia 324
 
2.2%
Other values (39) 11231
77.2%

Length

2024-06-06T20:10:36.051795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sydney 362
 
2.5%
darwin 335
 
2.3%
perth 333
 
2.3%
salmongums 331
 
2.3%
sydneyairport 328
 
2.3%
canberra 327
 
2.2%
townsville 326
 
2.2%
bendigo 325
 
2.2%
richmond 324
 
2.2%
watsonia 324
 
2.2%
Other values (39) 11231
77.2%

Most occurring characters

ValueCountFrequency (%)
a 11624
 
9.2%
r 11502
 
9.1%
o 10952
 
8.6%
e 10379
 
8.2%
n 9213
 
7.3%
l 7866
 
6.2%
i 7724
 
6.1%
t 5914
 
4.7%
d 3692
 
2.9%
s 3680
 
2.9%
Other values (30) 44331
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107133
84.4%
Uppercase Letter 19744
 
15.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11624
10.9%
r 11502
10.7%
o 10952
10.2%
e 10379
9.7%
n 9213
 
8.6%
l 7866
 
7.3%
i 7724
 
7.2%
t 5914
 
5.5%
d 3692
 
3.4%
s 3680
 
3.4%
Other values (12) 24587
22.9%
Uppercase Letter
ValueCountFrequency (%)
A 2785
14.1%
W 2459
12.5%
M 1805
9.1%
C 1791
9.1%
S 1615
8.2%
P 1529
7.7%
N 1322
6.7%
G 1232
 
6.2%
B 1184
 
6.0%
H 861
 
4.4%
Other values (8) 3161
16.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 126877
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11624
 
9.2%
r 11502
 
9.1%
o 10952
 
8.6%
e 10379
 
8.2%
n 9213
 
7.3%
l 7866
 
6.2%
i 7724
 
6.1%
t 5914
 
4.7%
d 3692
 
2.9%
s 3680
 
2.9%
Other values (30) 44331
34.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126877
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11624
 
9.2%
r 11502
 
9.1%
o 10952
 
8.6%
e 10379
 
8.2%
n 9213
 
7.3%
l 7866
 
6.2%
i 7724
 
6.1%
t 5914
 
4.7%
d 3692
 
2.9%
s 3680
 
2.9%
Other values (30) 44331
34.9%

MinTemp
Real number (ℝ)

MISSING 

Distinct352
Distinct (%)2.4%
Missing157
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean12.239926
Minimum-8
Maximum30.7
Zeros15
Zeros (%)0.1%
Negative326
Negative (%)2.2%
Memory size227.3 KiB
2024-06-06T20:10:36.256250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile2
Q17.7
median12
Q316.9
95-th percentile23
Maximum30.7
Range38.7
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.3729908
Coefficient of variation (CV)0.52067232
Kurtosis-0.46964078
Mean12.239926
Median Absolute Deviation (MAD)4.6
Skewness0.023234871
Sum176120.3
Variance40.615012
MonotonicityNot monotonic
2024-06-06T20:10:36.471672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.5 103
 
0.7%
12.7 102
 
0.7%
10.2 101
 
0.7%
11.6 97
 
0.7%
10.9 94
 
0.6%
10.8 93
 
0.6%
8 93
 
0.6%
13.8 90
 
0.6%
7.4 90
 
0.6%
11.8 89
 
0.6%
Other values (342) 13437
92.4%
(Missing) 157
 
1.1%
ValueCountFrequency (%)
-8 1
 
< 0.1%
-7.5 1
 
< 0.1%
-6.8 1
 
< 0.1%
-6.6 2
< 0.1%
-6.3 1
 
< 0.1%
-6.1 1
 
< 0.1%
-5.9 2
< 0.1%
-5.8 2
< 0.1%
-5.6 1
 
< 0.1%
-5.5 3
< 0.1%
ValueCountFrequency (%)
30.7 1
 
< 0.1%
29.2 1
 
< 0.1%
29 1
 
< 0.1%
28.8 2
< 0.1%
28.6 2
< 0.1%
28.5 2
< 0.1%
28.4 1
 
< 0.1%
28.3 3
< 0.1%
28.2 2
< 0.1%
28.1 2
< 0.1%

MaxTemp
Real number (ℝ)

Distinct432
Distinct (%)3.0%
Missing135
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean23.28998
Minimum-3.7
Maximum48.1
Zeros4
Zeros (%)< 0.1%
Negative9
Negative (%)0.1%
Memory size227.3 KiB
2024-06-06T20:10:36.694040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.7
5-th percentile12.9
Q118
median22.7
Q328.3
95-th percentile35.4
Maximum48.1
Range51.8
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation7.1098279
Coefficient of variation (CV)0.30527411
Kurtosis-0.2289349
Mean23.28998
Median Absolute Deviation (MAD)5.1
Skewness0.17971498
Sum335631.9
Variance50.549653
MonotonicityNot monotonic
2024-06-06T20:10:36.899491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 97
 
0.7%
19 97
 
0.7%
18.9 91
 
0.6%
22.3 91
 
0.6%
22.2 90
 
0.6%
20.5 90
 
0.6%
18.2 87
 
0.6%
18.4 86
 
0.6%
22.5 85
 
0.6%
17.8 85
 
0.6%
Other values (422) 13512
92.9%
(Missing) 135
 
0.9%
ValueCountFrequency (%)
-3.7 1
 
< 0.1%
-2.5 1
 
< 0.1%
-1.5 1
 
< 0.1%
-1.3 1
 
< 0.1%
-0.9 1
 
< 0.1%
-0.7 1
 
< 0.1%
-0.3 1
 
< 0.1%
-0.2 2
< 0.1%
0 4
< 0.1%
0.1 4
< 0.1%
ValueCountFrequency (%)
48.1 1
 
< 0.1%
46.8 1
 
< 0.1%
46.7 1
 
< 0.1%
45.1 1
 
< 0.1%
44.8 2
< 0.1%
44.5 1
 
< 0.1%
44.4 3
< 0.1%
44.3 1
 
< 0.1%
44.1 1
 
< 0.1%
43.9 2
< 0.1%

Rainfall
Real number (ℝ)

MISSING  ZEROS 

Distinct312
Distinct (%)2.2%
Missing330
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean2.3511466
Minimum0
Maximum367.6
Zeros9084
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:37.101530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile12.6
Maximum367.6
Range367.6
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.905705
Coefficient of variation (CV)3.7878136
Kurtosis285.94905
Mean2.3511466
Median Absolute Deviation (MAD)0
Skewness11.990502
Sum33423.9
Variance79.311581
MonotonicityNot monotonic
2024-06-06T20:10:37.318061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9084
62.5%
0.2 876
 
6.0%
0.4 398
 
2.7%
0.6 267
 
1.8%
0.8 199
 
1.4%
1.2 182
 
1.3%
1 182
 
1.3%
1.6 131
 
0.9%
1.4 130
 
0.9%
2.2 105
 
0.7%
Other values (302) 2662
 
18.3%
(Missing) 330
 
2.3%
ValueCountFrequency (%)
0 9084
62.5%
0.1 14
 
0.1%
0.2 876
 
6.0%
0.3 7
 
< 0.1%
0.4 398
 
2.7%
0.5 5
 
< 0.1%
0.6 267
 
1.8%
0.7 1
 
< 0.1%
0.8 199
 
1.4%
0.9 2
 
< 0.1%
ValueCountFrequency (%)
367.6 1
< 0.1%
206.2 1
< 0.1%
167 1
< 0.1%
155.8 1
< 0.1%
145.6 1
< 0.1%
144.2 1
< 0.1%
143.8 1
< 0.1%
142.2 1
< 0.1%
136.4 1
< 0.1%
128 1
< 0.1%

Evaporation
Real number (ℝ)

MISSING 

Distinct197
Distinct (%)2.4%
Missing6183
Missing (%)42.5%
Infinite0
Infinite (%)0.0%
Mean5.495217
Minimum0
Maximum65.4
Zeros26
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:37.516504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q12.6
median4.8
Q37.4
95-th percentile12.2
Maximum65.4
Range65.4
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.2108766
Coefficient of variation (CV)0.7662803
Kurtosis26.756564
Mean5.495217
Median Absolute Deviation (MAD)2.4
Skewness3.2838571
Sum45956.5
Variance17.731482
MonotonicityNot monotonic
2024-06-06T20:10:37.722953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 308
 
2.1%
8 258
 
1.8%
3.4 222
 
1.5%
2 215
 
1.5%
2.6 208
 
1.4%
2.2 203
 
1.4%
3 202
 
1.4%
3.6 197
 
1.4%
1.4 194
 
1.3%
2.8 194
 
1.3%
Other values (187) 6162
42.4%
(Missing) 6183
42.5%
ValueCountFrequency (%)
0 26
 
0.2%
0.2 57
 
0.4%
0.3 1
 
< 0.1%
0.4 83
0.6%
0.5 1
 
< 0.1%
0.6 108
0.7%
0.7 3
 
< 0.1%
0.8 154
1.1%
0.9 2
 
< 0.1%
1 167
1.1%
ValueCountFrequency (%)
65.4 1
< 0.1%
60.2 1
< 0.1%
58 1
< 0.1%
56.6 1
< 0.1%
50.8 1
< 0.1%
48.8 1
< 0.1%
48.4 1
< 0.1%
47.2 1
< 0.1%
43.4 1
< 0.1%
43 1
< 0.1%

Sunshine
Real number (ℝ)

MISSING  ZEROS 

Distinct143
Distinct (%)1.9%
Missing6904
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean7.5989924
Minimum0
Maximum14.3
Zeros244
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:37.953916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q14.8
median8.5
Q310.6
95-th percentile12.8
Maximum14.3
Range14.3
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation3.8023753
Coefficient of variation (CV)0.50037888
Kurtosis-0.8213867
Mean7.5989924
Median Absolute Deviation (MAD)2.6
Skewness-0.50634301
Sum58071.5
Variance14.458058
MonotonicityNot monotonic
2024-06-06T20:10:38.183336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 244
 
1.7%
10.3 121
 
0.8%
10.5 120
 
0.8%
10.7 113
 
0.8%
10.4 105
 
0.7%
9.2 102
 
0.7%
10.1 101
 
0.7%
9.8 100
 
0.7%
10.6 98
 
0.7%
10.8 98
 
0.7%
Other values (133) 6440
44.3%
(Missing) 6904
47.5%
ValueCountFrequency (%)
0 244
1.7%
0.1 57
 
0.4%
0.2 72
 
0.5%
0.3 44
 
0.3%
0.4 39
 
0.3%
0.5 42
 
0.3%
0.6 32
 
0.2%
0.7 25
 
0.2%
0.8 26
 
0.2%
0.9 33
 
0.2%
ValueCountFrequency (%)
14.3 2
 
< 0.1%
14.1 3
 
< 0.1%
14 5
 
< 0.1%
13.9 3
 
< 0.1%
13.8 7
 
< 0.1%
13.7 9
 
0.1%
13.6 18
0.1%
13.5 16
 
0.1%
13.4 32
0.2%
13.3 43
0.3%

WindGustDir
Categorical

MISSING 

Distinct16
Distinct (%)0.1%
Missing1056
Missing (%)7.3%
Memory size227.3 KiB
W
960 
N
958 
SE
945 
WSW
933 
SSE
932 
Other values (11)
8762 

Length

Max length3
Median length2
Mean length2.2031134
Min length1

Characters and Unicode

Total characters29720
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowN
3rd rowNE
4th rowSSW
5th rowW

Common Values

ValueCountFrequency (%)
W 960
 
6.6%
N 958
 
6.6%
SE 945
 
6.5%
WSW 933
 
6.4%
SSE 932
 
6.4%
E 907
 
6.2%
SSW 900
 
6.2%
SW 882
 
6.1%
S 857
 
5.9%
WNW 838
 
5.8%
Other values (6) 4378
30.1%
(Missing) 1056
 
7.3%

Length

2024-06-06T20:10:38.387779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w 960
 
7.1%
n 958
 
7.1%
se 945
 
7.0%
wsw 933
 
6.9%
sse 932
 
6.9%
e 907
 
6.7%
ssw 900
 
6.7%
sw 882
 
6.5%
s 857
 
6.4%
wnw 838
 
6.2%
Other values (6) 4378
32.5%

Most occurring characters

ValueCountFrequency (%)
S 8020
27.0%
W 7749
26.1%
E 7233
24.3%
N 6718
22.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 29720
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 8020
27.0%
W 7749
26.1%
E 7233
24.3%
N 6718
22.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 29720
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 8020
27.0%
W 7749
26.1%
E 7233
24.3%
N 6718
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 8020
27.0%
W 7749
26.1%
E 7233
24.3%
N 6718
22.6%

WindGustSpeed
Real number (ℝ)

MISSING 

Distinct56
Distinct (%)0.4%
Missing1048
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean40.025708
Minimum7
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:38.576306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile20
Q131
median39
Q348
95-th percentile65
Maximum135
Range128
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.388806
Coefficient of variation (CV)0.33450518
Kurtosis1.2994461
Mean40.025708
Median Absolute Deviation (MAD)8
Skewness0.83826509
Sum540267
Variance179.26014
MonotonicityNot monotonic
2024-06-06T20:10:38.788008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 918
 
6.3%
39 895
 
6.2%
31 835
 
5.7%
37 816
 
5.6%
33 798
 
5.5%
41 760
 
5.2%
30 730
 
5.0%
43 668
 
4.6%
28 646
 
4.4%
46 545
 
3.7%
Other values (46) 5887
40.5%
(Missing) 1048
 
7.2%
ValueCountFrequency (%)
7 1
 
< 0.1%
9 9
 
0.1%
11 13
 
0.1%
13 52
 
0.4%
15 86
 
0.6%
17 153
 
1.1%
19 175
1.2%
20 228
1.6%
22 256
1.8%
24 393
2.7%
ValueCountFrequency (%)
135 1
 
< 0.1%
109 2
 
< 0.1%
107 2
 
< 0.1%
104 2
 
< 0.1%
102 3
< 0.1%
100 2
 
< 0.1%
98 4
< 0.1%
96 2
 
< 0.1%
94 4
< 0.1%
93 5
< 0.1%

WindDir9am
Categorical

MISSING 

Distinct16
Distinct (%)0.1%
Missing1057
Missing (%)7.3%
Memory size227.3 KiB
N
1169 
SSE
956 
NW
924 
E
920 
SE
913 
Other values (11)
8607 

Length

Max length3
Median length2
Mean length2.1817777
Min length1

Characters and Unicode

Total characters29430
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowW
3rd rowN
4th rowSW
5th rowSW

Common Values

ValueCountFrequency (%)
N 1169
 
8.0%
SSE 956
 
6.6%
NW 924
 
6.4%
E 920
 
6.3%
SE 913
 
6.3%
S 879
 
6.0%
SW 863
 
5.9%
NNE 839
 
5.8%
W 814
 
5.6%
ENE 790
 
5.4%
Other values (6) 4422
30.4%
(Missing) 1057
 
7.3%

Length

2024-06-06T20:10:38.996524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 1169
 
8.7%
sse 956
 
7.1%
nw 924
 
6.9%
e 920
 
6.8%
se 913
 
6.8%
s 879
 
6.5%
sw 863
 
6.4%
nne 839
 
6.2%
w 814
 
6.0%
ene 790
 
5.9%
Other values (6) 4422
32.8%

Most occurring characters

ValueCountFrequency (%)
N 7530
25.6%
S 7515
25.5%
E 7515
25.5%
W 6870
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 29430
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 7530
25.6%
S 7515
25.5%
E 7515
25.5%
W 6870
23.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 29430
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 7530
25.6%
S 7515
25.5%
E 7515
25.5%
W 6870
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 7530
25.6%
S 7515
25.5%
E 7515
25.5%
W 6870
23.3%

WindDir3pm
Categorical

MISSING 

Distinct16
Distinct (%)0.1%
Missing411
Missing (%)2.8%
Memory size227.3 KiB
SE
1062 
W
1009 
S
1005 
WSW
954 
SW
938 
Other values (11)
9167 

Length

Max length3
Median length2
Mean length2.2067917
Min length1

Characters and Unicode

Total characters31193
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNW
2nd rowWNW
3rd rowN
4th rowNE
5th rowS

Common Values

ValueCountFrequency (%)
SE 1062
 
7.3%
W 1009
 
6.9%
S 1005
 
6.9%
WSW 954
 
6.6%
SW 938
 
6.4%
SSE 929
 
6.4%
NW 896
 
6.2%
N 888
 
6.1%
ESE 868
 
6.0%
E 842
 
5.8%
Other values (6) 4744
32.6%

Length

2024-06-06T20:10:39.189006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se 1062
 
7.5%
w 1009
 
7.1%
s 1005
 
7.1%
wsw 954
 
6.7%
sw 938
 
6.6%
sse 929
 
6.6%
nw 896
 
6.3%
n 888
 
6.3%
ese 868
 
6.1%
e 842
 
6.0%
Other values (6) 4744
33.6%

Most occurring characters

ValueCountFrequency (%)
S 8311
26.6%
W 8077
25.9%
E 7604
24.4%
N 7201
23.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 31193
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 8311
26.6%
W 8077
25.9%
E 7604
24.4%
N 7201
23.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 31193
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 8311
26.6%
W 8077
25.9%
E 7604
24.4%
N 7201
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 8311
26.6%
W 8077
25.9%
E 7604
24.4%
N 7201
23.1%

WindSpeed9am
Real number (ℝ)

MISSING  ZEROS 

Distinct34
Distinct (%)0.2%
Missing179
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean14.06856
Minimum0
Maximum67
Zeros869
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:39.390478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum67
Range67
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9376133
Coefficient of variation (CV)0.63528985
Kurtosis0.9350429
Mean14.06856
Median Absolute Deviation (MAD)6
Skewness0.7588526
Sum202123
Variance79.880932
MonotonicityNot monotonic
2024-06-06T20:10:39.578964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
9 1383
 
9.5%
13 1288
 
8.9%
11 1164
 
8.0%
17 1088
 
7.5%
15 1084
 
7.5%
7 1069
 
7.3%
19 880
 
6.0%
0 869
 
6.0%
6 860
 
5.9%
20 833
 
5.7%
Other values (24) 3849
26.5%
ValueCountFrequency (%)
0 869
6.0%
2 483
 
3.3%
4 650
4.5%
6 860
5.9%
7 1069
7.3%
9 1383
9.5%
11 1164
8.0%
13 1288
8.9%
15 1084
7.5%
17 1088
7.5%
ValueCountFrequency (%)
67 1
 
< 0.1%
63 1
 
< 0.1%
57 3
 
< 0.1%
56 5
 
< 0.1%
54 5
 
< 0.1%
52 7
 
< 0.1%
50 13
0.1%
48 9
0.1%
46 17
0.1%
44 21
0.1%

WindSpeed3pm
Real number (ℝ)

MISSING 

Distinct36
Distinct (%)0.3%
Missing302
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean18.718057
Minimum0
Maximum69
Zeros102
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:39.761448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median19
Q324
95-th percentile35
Maximum69
Range69
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.7995923
Coefficient of variation (CV)0.47011249
Kurtosis0.71014711
Mean18.718057
Median Absolute Deviation (MAD)6
Skewness0.6339216
Sum266620
Variance77.432824
MonotonicityNot monotonic
2024-06-06T20:10:39.970885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
17 1294
 
8.9%
13 1245
 
8.6%
15 1230
 
8.5%
20 1186
 
8.2%
19 1114
 
7.7%
11 963
 
6.6%
9 955
 
6.6%
24 863
 
5.9%
22 811
 
5.6%
26 693
 
4.8%
Other values (26) 3890
26.7%
ValueCountFrequency (%)
0 102
 
0.7%
2 91
 
0.6%
4 228
 
1.6%
6 396
 
2.7%
7 575
4.0%
9 955
6.6%
11 963
6.6%
13 1245
8.6%
15 1230
8.5%
17 1294
8.9%
ValueCountFrequency (%)
69 1
 
< 0.1%
65 1
 
< 0.1%
63 2
 
< 0.1%
61 3
 
< 0.1%
57 1
 
< 0.1%
56 10
0.1%
54 5
 
< 0.1%
52 9
0.1%
50 20
0.1%
48 16
0.1%

Humidity9am
Real number (ℝ)

MISSING 

Distinct98
Distinct (%)0.7%
Missing286
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean68.586676
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:40.171337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34
Q157
median69
Q383
95-th percentile98
Maximum100
Range99
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.033576
Coefficient of variation (CV)0.27751127
Kurtosis-0.065812412
Mean68.586676
Median Absolute Deviation (MAD)13
Skewness-0.46645356
Sum978046
Variance362.27701
MonotonicityNot monotonic
2024-06-06T20:10:40.370635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 325
 
2.2%
69 323
 
2.2%
66 319
 
2.2%
65 315
 
2.2%
62 302
 
2.1%
70 301
 
2.1%
72 299
 
2.1%
67 299
 
2.1%
100 295
 
2.0%
63 289
 
2.0%
Other values (88) 11193
76.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
4 1
 
< 0.1%
5 4
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 4
 
< 0.1%
9 5
 
< 0.1%
10 7
< 0.1%
11 13
0.1%
12 12
0.1%
ValueCountFrequency (%)
100 295
2.0%
99 325
2.2%
98 192
1.3%
97 161
1.1%
96 161
1.1%
95 163
1.1%
94 160
1.1%
93 181
1.2%
92 182
1.3%
91 205
1.4%

Humidity3pm
Real number (ℝ)

MISSING 

Distinct100
Distinct (%)0.7%
Missing457
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean51.454539
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:40.563684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q136
median52
Q366
95-th percentile88
Maximum100
Range99
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.931978
Coefficient of variation (CV)0.40680529
Kurtosis-0.52193895
Mean51.454539
Median Absolute Deviation (MAD)15
Skewness0.027823284
Sum724943
Variance438.14772
MonotonicityNot monotonic
2024-06-06T20:10:41.127672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 282
 
1.9%
60 282
 
1.9%
52 280
 
1.9%
56 278
 
1.9%
55 273
 
1.9%
63 267
 
1.8%
54 261
 
1.8%
61 255
 
1.8%
44 253
 
1.7%
47 251
 
1.7%
Other values (90) 11407
78.4%
(Missing) 457
 
3.1%
ValueCountFrequency (%)
1 5
 
< 0.1%
2 1
 
< 0.1%
3 4
 
< 0.1%
4 14
 
0.1%
5 14
 
0.1%
6 25
0.2%
7 32
0.2%
8 52
0.4%
9 50
0.3%
10 55
0.4%
ValueCountFrequency (%)
100 50
0.3%
99 46
0.3%
98 61
0.4%
97 33
0.2%
96 49
0.3%
95 45
0.3%
94 59
0.4%
93 62
0.4%
92 67
0.5%
91 62
0.4%

Pressure9am
Real number (ℝ)

MISSING 

Distinct439
Distinct (%)3.4%
Missing1537
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean1017.6155
Minimum982.3
Maximum1040.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:41.321715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum982.3
5-th percentile1006.4
Q11013
median1017.6
Q31022.3
95-th percentile1029.4
Maximum1040.4
Range58.1
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation7.0069934
Coefficient of variation (CV)0.0068856985
Kurtosis0.226705
Mean1017.6155
Median Absolute Deviation (MAD)4.7
Skewness-0.083901448
Sum13238160
Variance49.097957
MonotonicityNot monotonic
2024-06-06T20:10:41.519218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1017.2 95
 
0.7%
1015.7 88
 
0.6%
1019.6 87
 
0.6%
1015.1 87
 
0.6%
1018.4 87
 
0.6%
1016.4 86
 
0.6%
1015.2 85
 
0.6%
1018.7 84
 
0.6%
1018 84
 
0.6%
1015.5 83
 
0.6%
Other values (429) 12143
83.5%
(Missing) 1537
 
10.6%
ValueCountFrequency (%)
982.3 1
< 0.1%
984.6 1
< 0.1%
989.3 1
< 0.1%
989.4 1
< 0.1%
990.2 1
< 0.1%
991.6 1
< 0.1%
991.7 2
< 0.1%
991.8 1
< 0.1%
991.9 1
< 0.1%
992.1 2
< 0.1%
ValueCountFrequency (%)
1040.4 1
 
< 0.1%
1039.3 1
 
< 0.1%
1039.1 1
 
< 0.1%
1039 1
 
< 0.1%
1038.8 2
< 0.1%
1038.5 1
 
< 0.1%
1038.3 1
 
< 0.1%
1038 2
< 0.1%
1037.7 3
< 0.1%
1037.6 1
 
< 0.1%

Pressure3pm
Real number (ℝ)

MISSING 

Distinct432
Distinct (%)3.3%
Missing1522
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean1015.2199
Minimum984.2
Maximum1037.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:41.718658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum984.2
5-th percentile1004.115
Q11010.5
median1015.2
Q31019.9
95-th percentile1026.7
Maximum1037.7
Range53.5
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation6.9060773
Coefficient of variation (CV)0.0068025434
Kurtosis0.082146068
Mean1015.2199
Median Absolute Deviation (MAD)4.7
Skewness-0.022871781
Sum13222224
Variance47.693904
MonotonicityNot monotonic
2024-06-06T20:10:41.948595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014.6 94
 
0.6%
1015.5 88
 
0.6%
1017.4 87
 
0.6%
1014.4 83
 
0.6%
1010.9 83
 
0.6%
1015.6 82
 
0.6%
1016 82
 
0.6%
1016.3 82
 
0.6%
1012.3 81
 
0.6%
1013.9 81
 
0.6%
Other values (422) 12181
83.7%
(Missing) 1522
 
10.5%
ValueCountFrequency (%)
984.2 1
< 0.1%
985.1 1
< 0.1%
986 1
< 0.1%
986.4 1
< 0.1%
986.8 1
< 0.1%
986.9 1
< 0.1%
987.1 2
< 0.1%
987.4 1
< 0.1%
988.3 1
< 0.1%
991.1 1
< 0.1%
ValueCountFrequency (%)
1037.7 1
 
< 0.1%
1036.4 1
 
< 0.1%
1035.9 2
< 0.1%
1035.8 1
 
< 0.1%
1035.7 1
 
< 0.1%
1035.6 3
< 0.1%
1035.5 1
 
< 0.1%
1035.4 1
 
< 0.1%
1035.3 3
< 0.1%
1035.1 1
 
< 0.1%

Cloud9am
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.1%
Missing5558
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean4.4185581
Minimum0
Maximum9
Zeros852
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:42.138460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.879181
Coefficient of variation (CV)0.651611
Kurtosis-1.5420738
Mean4.4185581
Median Absolute Deviation (MAD)3
Skewness-0.21291739
Sum39714
Variance8.2896834
MonotonicityNot monotonic
2024-06-06T20:10:42.301063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 1966
 
13.5%
1 1617
 
11.1%
8 1440
 
9.9%
0 852
 
5.9%
6 826
 
5.7%
2 658
 
4.5%
5 608
 
4.2%
3 586
 
4.0%
4 434
 
3.0%
9 1
 
< 0.1%
(Missing) 5558
38.2%
ValueCountFrequency (%)
0 852
5.9%
1 1617
11.1%
2 658
 
4.5%
3 586
 
4.0%
4 434
 
3.0%
5 608
 
4.2%
6 826
5.7%
7 1966
13.5%
8 1440
9.9%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1440
9.9%
7 1966
13.5%
6 826
5.7%
5 608
 
4.2%
4 434
 
3.0%
3 586
 
4.0%
2 658
 
4.5%
1 1617
11.1%
0 852
5.9%

Cloud3pm
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.1%
Missing5875
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean4.5044401
Minimum0
Maximum8
Zeros498
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size227.3 KiB
2024-06-06T20:10:42.456646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7215569
Coefficient of variation (CV)0.60419427
Kurtosis-1.4620243
Mean4.5044401
Median Absolute Deviation (MAD)2
Skewness-0.22053708
Sum39058
Variance7.4068719
MonotonicityNot monotonic
2024-06-06T20:10:42.658107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 1838
 
12.6%
1 1509
 
10.4%
8 1274
 
8.8%
6 898
 
6.2%
2 743
 
5.1%
3 706
 
4.9%
5 679
 
4.7%
4 526
 
3.6%
0 498
 
3.4%
(Missing) 5875
40.4%
ValueCountFrequency (%)
0 498
 
3.4%
1 1509
10.4%
2 743
5.1%
3 706
 
4.9%
4 526
 
3.6%
5 679
 
4.7%
6 898
6.2%
7 1838
12.6%
8 1274
8.8%
ValueCountFrequency (%)
8 1274
8.8%
7 1838
12.6%
6 898
6.2%
5 679
 
4.7%
4 526
 
3.6%
3 706
 
4.9%
2 743
5.1%
1 1509
10.4%
0 498
 
3.4%

Temp9am
Real number (ℝ)

MISSING 

Distinct384
Distinct (%)2.7%
Missing194
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean17.066325
Minimum-5.2
Maximum38.9
Zeros5
Zeros (%)< 0.1%
Negative43
Negative (%)0.3%
Memory size227.3 KiB
2024-06-06T20:10:42.858533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5.2
5-th percentile7
Q112.4
median16.8
Q321.6
95-th percentile28.2
Maximum38.9
Range44.1
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.4528251
Coefficient of variation (CV)0.37810278
Kurtosis-0.32940978
Mean17.066325
Median Absolute Deviation (MAD)4.6
Skewness0.072492503
Sum244935.9
Variance41.638952
MonotonicityNot monotonic
2024-06-06T20:10:43.102499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.8 106
 
0.7%
15.1 103
 
0.7%
16 99
 
0.7%
14.9 98
 
0.7%
13.8 97
 
0.7%
17.3 96
 
0.7%
14.5 95
 
0.7%
13.2 95
 
0.7%
14.1 93
 
0.6%
15.8 93
 
0.6%
Other values (374) 13377
92.0%
(Missing) 194
 
1.3%
ValueCountFrequency (%)
-5.2 2
< 0.1%
-4.9 1
< 0.1%
-4.3 1
< 0.1%
-3.6 2
< 0.1%
-3.4 1
< 0.1%
-3.3 1
< 0.1%
-3.1 2
< 0.1%
-2.9 1
< 0.1%
-2.7 1
< 0.1%
-2.4 1
< 0.1%
ValueCountFrequency (%)
38.9 1
< 0.1%
37.6 1
< 0.1%
37.5 2
< 0.1%
36.4 1
< 0.1%
36.2 1
< 0.1%
36.1 1
< 0.1%
35.7 1
< 0.1%
35.5 1
< 0.1%
35.3 1
< 0.1%
35.2 1
< 0.1%

Temp3pm
Real number (ℝ)

MISSING 

Distinct424
Distinct (%)3.0%
Missing361
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean21.735361
Minimum-4.1
Maximum46.1
Zeros3
Zeros (%)< 0.1%
Negative20
Negative (%)0.1%
Memory size227.3 KiB
2024-06-06T20:10:43.321955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.1
5-th percentile11.6
Q116.7
median21.2
Q326.5
95-th percentile33.6
Maximum46.1
Range50.2
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.9241342
Coefficient of variation (CV)0.3185654
Kurtosis-0.13906146
Mean21.735361
Median Absolute Deviation (MAD)4.8
Skewness0.18822357
Sum308316.1
Variance47.943634
MonotonicityNot monotonic
2024-06-06T20:10:43.535881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.3 97
 
0.7%
18.5 97
 
0.7%
21.2 96
 
0.7%
16.8 96
 
0.7%
18.4 96
 
0.7%
19.4 95
 
0.7%
17.5 91
 
0.6%
17.3 90
 
0.6%
18.9 89
 
0.6%
22.6 88
 
0.6%
Other values (414) 13250
91.1%
(Missing) 361
 
2.5%
ValueCountFrequency (%)
-4.1 1
 
< 0.1%
-3.3 1
 
< 0.1%
-3.1 1
 
< 0.1%
-3 1
 
< 0.1%
-2.9 1
 
< 0.1%
-2.3 1
 
< 0.1%
-2.2 1
 
< 0.1%
-2.1 1
 
< 0.1%
-1.6 1
 
< 0.1%
-1.5 3
< 0.1%
ValueCountFrequency (%)
46.1 3
< 0.1%
43.9 1
 
< 0.1%
43.4 1
 
< 0.1%
43.2 1
 
< 0.1%
43 1
 
< 0.1%
42.6 1
 
< 0.1%
42.4 1
 
< 0.1%
42.3 1
 
< 0.1%
42.2 2
< 0.1%
42 3
< 0.1%

RainToday
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing330
Missing (%)2.3%
Memory size142.1 KiB
False
11035 
True
3181 
(Missing)
 
330
ValueCountFrequency (%)
False 11035
75.9%
True 3181
 
21.9%
(Missing) 330
 
2.3%
2024-06-06T20:10:43.733272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

RainTomorrow
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing329
Missing (%)2.3%
Memory size142.1 KiB
False
11010 
True
3207 
(Missing)
 
329
ValueCountFrequency (%)
False 11010
75.7%
True 3207
 
22.0%
(Missing) 329
 
2.3%
2024-06-06T20:10:43.871962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2024-06-06T20:10:31.720721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:52.488281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:55.893301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:58.538819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:01.474006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:04.201355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:06.500226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:08.904656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:11.835939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:14.171806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:16.640727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:19.616180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:22.234622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:24.833912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:26.892456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:29.465206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:31.845361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:52.753086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:56.052872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:58.724322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:01.690935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:04.347005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:06.656394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:09.047314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:11.993518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:14.330387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:16.800262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:19.764314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:22.431608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:24.971551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:27.030650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:29.618304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:31.969537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:52.940115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:56.186079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:58.992126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:01.864469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:04.469677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:06.791032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:09.181302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:12.145077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:14.484731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:17.197231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:19.903410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:22.589684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:25.103461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:27.153325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:29.773888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:32.129437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:53.095700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:56.326214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:59.164664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:02.018095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:04.613840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:06.930647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:09.345905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:12.308643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:14.639359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:17.442548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:20.063521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:22.767173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:25.258045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:27.288962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:29.967369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:32.288015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:53.239316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:56.460387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:59.353771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:02.167695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:04.745524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:07.064264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:09.529650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:12.460260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:14.784448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:17.705382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:20.191179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:22.912425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:25.411596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:27.419681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:30.147887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:32.444664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:53.394927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:56.605482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:59.568215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:02.331258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:04.877762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:07.196908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:09.803865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:12.612439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:14.920271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:17.862961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:20.324821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:23.055697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:25.544242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:27.555853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:30.300478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:32.564924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:54.470502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:56.760068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:59.763737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:02.494294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:05.018394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:07.324594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:10.054736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:12.751661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:15.068369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:18.018568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:20.472411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:23.190340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:25.676651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:27.677542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:30.436622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:32.717561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:54.610165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:56.969512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:59.934254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:02.650620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:05.162010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:07.467791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:10.230458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:12.891702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:15.225417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:18.153235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:20.681155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:23.329926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:25.809296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:27.816721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:30.573256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:32.861206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:54.746763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:57.232808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:00.106687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:02.834783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:05.325908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:07.599644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:10.395524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:13.032354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:15.393993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:18.297858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:20.843707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:23.476534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:25.939975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:27.956306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:30.705902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:32.986842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:54.873424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:57.406343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:00.260277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:03.016261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:05.472516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:07.916741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:10.549115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:13.161009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:15.537621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:18.453405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:20.979344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:23.647079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:26.057689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:28.082993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:30.841538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:33.111547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:55.006069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:57.613785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:00.403920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:03.184024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:05.608181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:08.042367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:10.706255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:13.290591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:15.673219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:18.636158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:21.118974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:23.867490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:26.181358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:28.548723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:30.972695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:33.271119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:55.147691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:57.760881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:00.736190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:03.346632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:05.740673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:08.175044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:10.866825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:13.425777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:15.835785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:18.861555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:21.276185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:24.090430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:26.301011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:28.710795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:31.114342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:33.413742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:55.279339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:57.906509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:00.872415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:03.499200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:05.868362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:08.310245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:11.024441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:13.560432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:16.011316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:19.058532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:21.417832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:24.219137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:26.416728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:28.850421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:31.245991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:33.540362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:55.427544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:58.055112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:00.999698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:03.651803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:06.006054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:08.457889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:11.253793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:13.695082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:16.151977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:19.186735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:21.556472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:24.351140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:26.530425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:29.004015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:31.360684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:33.690589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:55.581478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:58.237624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:01.173263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:03.802390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:06.170681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:08.615466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:11.514096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:13.865627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:16.359385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:19.347268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:21.723027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:24.557650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:26.669065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:29.171979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:31.493329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:33.827269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:55.713745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:09:58.386234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:01.339986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:04.061697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:06.333047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:08.774005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:11.689295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:14.021173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:16.501103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:19.494900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:22.007247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:24.711241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:26.782749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:29.323075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-06T20:10:31.606001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-06-06T20:10:34.057673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-06T20:10:34.545972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-06T20:10:35.021699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
1007212012-04-22MountGambier15.018.94.26.68.0NNW54.0NNWNW24.031.073.064.01005.21003.74.05.017.317.6YesYes
302342008-03-30Sydney13.126.80.04.610.9NaNNaNWWNW22.015.061.022.01013.01009.00.01.016.925.9NoNo
684272011-12-10Melbourne19.029.0NaN11.05.6N59.0NN39.022.050.038.01006.51003.4NaNNaN24.227.2NaNNaN
286242013-03-27Richmond18.132.20.02.1NaNNE30.0NaNNE0.017.099.051.01019.21014.6NaNNaN20.931.6NoNo
311732010-10-25Sydney13.919.614.01.25.4SSW50.0SWS17.022.090.064.0NaN1018.47.06.015.519.1YesNo
5732010-06-27Albury0.611.90.2NaNNaNW22.0SWNW2.011.099.054.01024.31021.81.02.02.811.5NoNo
1200562015-11-22PerthAirport21.836.20.012.211.9E61.0NNENW31.030.014.012.01014.91013.26.00.031.233.4NoNo
430572010-04-25Wollongong17.722.3NaNNaNNaNSSE54.0NaNSNaN37.0NaN80.01012.81014.3NaN7.0NaN17.0NaNYes
289662014-03-04Richmond18.425.72.61.2NaNNE24.0NaNNNE0.02.0100.064.01026.51023.3NaNNaN19.824.2YesNo
906912010-02-25GoldCoast22.027.916.6NaNNaNSSE61.0SSES31.030.077.078.01021.41020.9NaNNaN25.323.8YesYes
DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
472332012-06-03Canberra7.714.817.60.84.1SW24.0SSES6.013.088.065.01016.91012.18.0NaN9.314.4YesNo
1326432016-06-03Hobart1.211.00.00.83.5NNW33.0NWNNW20.013.084.057.01032.51029.77.01.03.110.8NoNo
482132015-04-08Canberra4.915.753.2NaNNaNSW48.0SSWS19.019.067.057.01013.01016.2NaN8.013.214.8YesNo
1169472015-08-14PearceRAAF5.123.40.0NaN9.8N26.0NEN9.013.076.045.01024.81020.9NaNNaN13.622.8NoNo
698642016-01-14MelbourneNaNNaNNaN17.61.1S54.0SSWSSW20.031.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
279832011-03-28Richmond12.424.10.28.4NaNE31.0NaNESE0.011.099.058.01027.51025.4NaNNaN15.823.5NoNo
103992012-10-03CoffsHarbour8.622.00.06.211.4ENE37.0ESENE13.026.045.054.01028.51023.51.00.019.821.2NoNo
627922013-08-27Sale5.818.50.02.66.0SW19.0NNWS9.02.076.053.01021.11017.21.08.012.818.3NoNo
319852013-04-13Sydney15.725.40.04.810.7NE35.0WNE17.022.071.055.01021.51016.41.01.019.623.7NoNo
534382012-10-07MountGinini-4.510.815.6NaNNaNS52.0SNW17.07.069.063.0NaNNaNNaNNaN-0.89.6YesNo